In general, the discriminative classifiers consistently yielded better performance than the GMM (standard and semi-supervised), both in terms of decision accuracy, robustness to sampling bias, and the number of queries made. In contrast, the decision-making performance of the semi-supervised generative models were highly dependent on the assumed distributions used for density estimation and the underlying distribution of data. Based on these results, faced with the conundrum of selecting a classifier to be used in a decision-supporting SHM system, an engineer may opt to use a discriminative classifier without second thought. However, the selection may require more nuanced deliberations. For example, digital twins are a form of asset management technology highly applicable to structures and infrastructure [1]}, [2]}, [3]}. In this context, SHM systems are a necessary component of digital twins. Furthermore, recent work has identified generative models as being a core component of digital twin technology [4]}. It follows that, if the context in which SHM system is being applied necessitates a generative model, such as within a digital twin, a discriminative model can be deemed an unsuitable choice of classifier. In this scenario, one may have to accept the effects of sampling bias, or find a method of accounting for them within a generative model. If a priori knowledge of data distributions is available (such as via expert elicitation, or transfer learning [5]}), then density estimations can be carefully selected such that EM, latent-state smoothing, or an alternative approach to semi-supervision can be incorporated into the risk-based active learning algorithm.
